| id: EVChargeEnv-v0 | |
| name: EVChargeEnv | |
| version: "0.1.0" | |
| description: > | |
| EVChargeEnv is a continuous-control electric vehicle charging environment | |
| with dynamic pricing, fluctuating grid load, and multi-objective reward signals. | |
| It is suitable for benchmarking agentic behavior and testing adaptation | |
| to non-stationary conditions. | |
| authors: | |
| - name: Ozan Özayranci | |
| github: "https://github.com/oozan" | |
| license: mit | |
| environment: | |
| observation_space: | |
| shape: [4] | |
| type: box | |
| description: | |
| - charge_level (0–1) | |
| - price (0–1) | |
| - grid_load (0–1) | |
| - time_step_norm (0–1) | |
| action_space: | |
| shape: [1] | |
| type: box | |
| description: continuous charge rate (0–1) | |
| reward_components: | |
| - progress_reward | |
| - cost_penalty | |
| - overload_penalty | |
| - time_penalty | |
| termination_conditions: | |
| - charge >= 1.0 | |
| - max_steps reached | |
| scenarios: | |
| - easy | |
| - medium | |
| - hard | |
| entry_point: env.ev_charge_env:EVChargeEnv | |
| tags: | |
| - energy | |
| - control | |
| - continuous | |
| - stochastic | |
| - reinforcement-learning | |
| - openenv | |